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Erschienen in: The Journal of Real Estate Finance and Economics 1-2/2020

Open Access 08.08.2019

Appraisal Accuracy and Automated Valuation Models in Rural Areas

verfasst von: Alexander N. Bogin, Jessica Shui

Erschienen in: The Journal of Real Estate Finance and Economics | Ausgabe 1-2/2020

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Abstract

Accurate and unbiased property value estimates are essential to credit risk management. Along with loan amount, they determine a mortgage’s loan-to-value ratio, which captures the degree of homeowner equity and is a key determinant of borrower credit risk. For home purchases, lenders generally require an independent appraisal, which, in addition to a home’s sales price, is used to calculate a value for the underlying collateral. A number of empirical studies have shown that property appraisals tend to be biased upwards, and over 90 percent of the time, either confirm or exceed the associated contract price. Our data suggest that appraisal bias is particularly pervasive in rural areas where over 25 percent of rural properties are appraised at more than five percent above contract price. Given this significant upward bias, we examine a host of alternate valuation techniques to more accurately estimate rural property values.

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Fußnoten
1
Cho and Megbolugbe (1996), Horne and Rosenblatt (1996), and Calem et al. (2017) find that between 90 to 95% of appraisals come in at or above contract price. See Yiu et al. (2006) for a detailed review of the literature. Consistent with existing literature, we define appraisal bias as the percentage deviation of the appraised value from the contract price. For an individual appraisal, it is possible that the appraised value exceeding the contract price is in fact an accurate estimate of the real house value. However, it is highly unlikely to observe such a systematically skewed relationship without at least some level of bias.
 
2
Fout and Yao (2016) find that about 32% of negative appraisals result in the transaction falling through.
 
3
Dotzour (1990) finds that the sales comparison approach tends to provide a more accurate measure of value than the cost approach.
 
4
Compared to AVM estimates in urban areas, AVM estimates in rural areas may face additional scrutiny due to lack of data.
 
5
We do not seek to use contemporaneous data and make real-time AVM predictions in this research. Instead, our work is focused on highlighting the pros and cons of machine learning algorithms and comparing their performance with more traditional methodologies.
 
6
Many other researchers, for example Pace and Hayunga (2018) and Villupuram and Johnson (2018), have also explored machine learning algorithms in property valuations.
 
7
This definition is consistent with the Enterprises’ guidance.
 
8
The same appraisal is often submitted to both Fannie Mae and Freddie Mac.
 
9
We apply a series of standard data filters, which include censoring observations associated with extreme/implausible values for several structural attributes (i.e., number bedrooms, number of bathrooms, square footage, and age). To further minimize the influence of outliers, we remove observations with sales prices in the top and bottom 1% of the price distribution.
 
10
This is about $55,000 lower than the average sales price across all purchase-money mortgages in the UAD.
 
11
This is about twice as old as the average property in the full purchase-money mortgage sample in the UAD.
 
12
In this paper, we use model and algorithm interchangeably to refer to each specific AVM technique.
 
13
As detailed, R2 are actually higher in the tails of the price distribution. While this result may seem counterintuitive, it is simply a reflection of the proportional nature of the statistic. Both the residual sum of squares and total sum of squares increase as we move away from the middle of the price distribution, but the total sum of squares increases at a faster rate. In other words, absolute fit (as captured by RMSE) is deteriorating, but proportional fit (or the percentage of explained variation) is actually increasing.
 
14
If using lambda.min, then the out-of-sample R2 value is 0.6803 and the RMSE is 0.3188.
 
15
Oftentimes, m is set equal to the square root of p.
 
16
Valuation bias is not unique to the real estate industry. Michaely and Womack (1999), White (2010), and Bolton et al. (2007) have examined similar market pressures and their varied impact on other financial sectors.
 
Literatur
Zurück zum Zitat Agarwal, S., Ben-David, I., & Yao, V. (2015). Collateral valuation and borrower financial constraints: Evidence from the residential real estate market. Management Science, 61(9), 2220–2240.CrossRef Agarwal, S., Ben-David, I., & Yao, V. (2015). Collateral valuation and borrower financial constraints: Evidence from the residential real estate market. Management Science, 61(9), 2220–2240.CrossRef
Zurück zum Zitat Blackburn, M., & Vermilyea, T. (2007). The role of information externalities and scale economies in home mortgage lending decisions. Journal of Urban Economics, 61(1), 71–85.CrossRef Blackburn, M., & Vermilyea, T. (2007). The role of information externalities and scale economies in home mortgage lending decisions. Journal of Urban Economics, 61(1), 71–85.CrossRef
Zurück zum Zitat Bolton, P., Freixas, X., & Shapiro, J. (2007). Conflicts of interest, information provision, and competition in the financial services industry. Journal of Financial Economics, 85(2), 297–330.CrossRef Bolton, P., Freixas, X., & Shapiro, J. (2007). Conflicts of interest, information provision, and competition in the financial services industry. Journal of Financial Economics, 85(2), 297–330.CrossRef
Zurück zum Zitat Calem, P. S., Lambie-Hanson, L., & Nakamura, L. I. (2017). Appraising home purchase appraisals.CrossRef Calem, P. S., Lambie-Hanson, L., & Nakamura, L. I. (2017). Appraising home purchase appraisals.CrossRef
Zurück zum Zitat Cho, M., & Megbolugbe, I. F. (1996). An empirical analysis of property appraisal and mortgage redlining. The Journal of Real Estate Finance and Economics, 13(1), 45–55.CrossRef Cho, M., & Megbolugbe, I. F. (1996). An empirical analysis of property appraisal and mortgage redlining. The Journal of Real Estate Finance and Economics, 13(1), 45–55.CrossRef
Zurück zum Zitat Ding, L. (2014). The pattern of appraisal Bias in the Third District during the housing crisis. Philadelphia Federal Reserve: Working Paper. Ding, L. (2014). The pattern of appraisal Bias in the Third District during the housing crisis. Philadelphia Federal Reserve: Working Paper.
Zurück zum Zitat Dotzour, M. (1990). An empirical analysis of the reliability and precision of the cost approach in residential appraisal. Journal of Real Estate Research, 5(1), 67–74. Dotzour, M. (1990). An empirical analysis of the reliability and precision of the cost approach in residential appraisal. Journal of Real Estate Research, 5(1), 67–74.
Zurück zum Zitat Eriksen, M. D., Fout, H. B., Palim, M., & Rosenblatt, E. (2016). Contract price confirmation bias: Evidence from repeat appraisals. Working paper. Eriksen, M. D., Fout, H. B., Palim, M., & Rosenblatt, E. (2016). Contract price confirmation bias: Evidence from repeat appraisals. Working paper.
Zurück zum Zitat Fout, H., & Yao, V. (2016). Housing market effects of appraising below contract. Working paper. Fout, H., & Yao, V. (2016). Housing market effects of appraising below contract. Working paper.
Zurück zum Zitat Horne, D., & Rosenblatt, E. (1996). Property appraisals and moral hazard. Working paper. Horne, D., & Rosenblatt, E. (1996). Property appraisals and moral hazard. Working paper.
Zurück zum Zitat Kelly, A. (2007). Appraisals, automated valuation models, and mortgage default. Federal Housing Finance Agency: Working Paper. Kelly, A. (2007). Appraisals, automated valuation models, and mortgage default. Federal Housing Finance Agency: Working Paper.
Zurück zum Zitat LaCour-Little, M., & Malpezzi, S. (2003). Appraisal quality and residential mortgage default: Evidence from Alaska. The Journal of Real Estate Finance and Economics, 27(2), 211–233.CrossRef LaCour-Little, M., & Malpezzi, S. (2003). Appraisal quality and residential mortgage default: Evidence from Alaska. The Journal of Real Estate Finance and Economics, 27(2), 211–233.CrossRef
Zurück zum Zitat Lang, W. W., & Nakamura, L. I. (1993). A model of redlining. Journal of Urban Economics, 33(2), 223–234.CrossRef Lang, W. W., & Nakamura, L. I. (1993). A model of redlining. Journal of Urban Economics, 33(2), 223–234.CrossRef
Zurück zum Zitat Michaely, R., & Womack, K. L. (1999). Conflict of interest and the credibility of underwriter analyst recommendations. The Review of Financial Studies, 12(4), 653–686.CrossRef Michaely, R., & Womack, K. L. (1999). Conflict of interest and the credibility of underwriter analyst recommendations. The Review of Financial Studies, 12(4), 653–686.CrossRef
Zurück zum Zitat Pace, K., & Hayunga D. (2018). Combining random forests with spatiotemporal modeling to improve prediction of real estate prices. Working paper. Pace, K., & Hayunga D. (2018). Combining random forests with spatiotemporal modeling to improve prediction of real estate prices. Working paper.
Zurück zum Zitat Villupuram, S., & Johnson, E. (2018). The value of curb appeal: A machine learning approach. Working paper. Villupuram, S., & Johnson, E. (2018). The value of curb appeal: A machine learning approach. Working paper.
Zurück zum Zitat White, L. J. (2010). Credit-rating agencies and the financial crisis: Less regulation of CRAs is a better response. Journal of international banking law, 25(4), 170. White, L. J. (2010). Credit-rating agencies and the financial crisis: Less regulation of CRAs is a better response. Journal of international banking law, 25(4), 170.
Zurück zum Zitat Yiu, C. Y., Tang, B. S., Chiang, Y. H., & Choy, L. H. T. (2006). Alternative theories of appraisal Bias. Journal of Real Estate Literature, 14(3), 321–344. Yiu, C. Y., Tang, B. S., Chiang, Y. H., & Choy, L. H. T. (2006). Alternative theories of appraisal Bias. Journal of Real Estate Literature, 14(3), 321–344.
Metadaten
Titel
Appraisal Accuracy and Automated Valuation Models in Rural Areas
verfasst von
Alexander N. Bogin
Jessica Shui
Publikationsdatum
08.08.2019
Verlag
Springer US
Erschienen in
The Journal of Real Estate Finance and Economics / Ausgabe 1-2/2020
Print ISSN: 0895-5638
Elektronische ISSN: 1573-045X
DOI
https://doi.org/10.1007/s11146-019-09712-0

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